pyuncertainnumber.pba.dependency¶

Classes¶

Dependency

Dependency class to specify copula models.

Functions¶

`supported_family_check`(c)	check if copula family is supported
`empirical_copula`(data)	compute the empirical copula
`pl_3d_copula`(U, V, Z)

Module Contents¶

class pyuncertainnumber.pba.dependency.Dependency(family: str, params: numbers.Number | None = None, **kwargs)¶

Dependency class to specify copula models.

Parameters:

family (str) – Name of the copula family, one of “gaussian”, “t”, “frank”, “gumbel”, “clayton”, “independence”.
params (Number | None) – Backward-compatible single-parameter shortcut: - gaussian/t: interpreted as corr - frank/gumbel/clayton: interpreted as theta - independence: ignored
**kwargs – Any keyword parameters supported by the selected copula, e.g. corr=…, df=…, theta=…, k_dim=…, allow_singular=…

Examples

>>> Dependency("gaussian", params=0.8, k_dim=3)          # legacy style
>>> Dependency("gaussian", corr=0.8, k_dim=3)            # explicit
>>> Dependency("t", corr=0.6, df=5, k_dim=4)
>>> Dependency("frank", theta=2.5, k_dim=2)
>>> Dependency("independence", k_dim=5)

copulas_dict¶

_single_param_alias¶

family = ''¶

params = None¶

_copula¶

property copula¶: Access the underlying statsmodels copula instance.

_post_init_check()¶

__repr__()¶

pdf(u)¶

cdf(u)¶

u_sample(n: int, random_state=None)¶: draws n samples in the U space (unit hypercube)

display(style='3d_cdf', ax=None)¶: show the PDF or CDF in the u space

fit(data)¶

pyuncertainnumber.pba.dependency.supported_family_check(c)¶: check if copula family is supported

pyuncertainnumber.pba.dependency.empirical_copula(data)¶: compute the empirical copula

pyuncertainnumber.pba.dependency.pl_3d_copula(U, V, Z)¶